Robotic automation process (RPA) is defined as the automated processing of digitized business processes by computer programs. The tasks may include queries, calculations, or the maintenance of input forms or transactions. Business processes are only repetitive and rule-based processes or routine tasks that are usually performed by humans. In process automation software programs take on the roles and tasks of the users and interact in the same way, which is why we also like to talk about software robots or bots. Examples from the insurance area are the handling and initial notification in the regulation of damage cases or the automatic data maintenance for registration and change processes with customers.
The strengths of RPA are, in addition to relieving the staff of routine activities, that automated tasks are carried out faster, error-free, reliable and thus highly profitable. Unfortunately, the robots used are only as good as their underlying rules and thus completely deterministic. They are not able to react flexibly to deviations or variants, for example, if process input data are not clearly structured or if decisions within the process are complex and non-linear.
AI uses a probabilistic approach. By mitigating the caveat that robots need structured data as input, Artificial Intelligence can help transform data streams with many degrees of freedom into a structured format. Are the input data unstructured or only partially structuredâââeg. B. different invoice layouts of different suppliers, but which largely contain the same informationâââcan be transformed by means of artificial intelligence methods such as deep learning or alternatives of machine learning this data stream in a structured format, even if the text is written in free-form language
Addressing complexity constraints in decisions (be it a machine learning model or a rule engine) can make the RPA process more flexible and overall. Corresponding programs often work with multiple subsystems and different techniques, which store knowledge and experience of experts in an internal knowledge model. This model is then queried during decision making to find the optimal answer. Such an approach can look at many different input variables, each with their individual impact or weighting, to condense it into an end result, e.g. For example, whether a loan application should be approved on the basis of many parameters. An essential difference between the procedures lies in the desirability or comprehensibility of made decisions. As a rule, good results can already be obtained with decision tree methods and without hard-to-interpret methods such as deep learning. Finally, the combination of RPA and AI allows for a very flexible approach to process automation, which can vary depending on the target direction.
In the end, both technologies are perfect pairs as they complement each other very well due to the different focus, so that they merge from the userâs point of view into Intelligent Automation (IA).
Customers of the FeatSystems understand the value of Robotic Process Automation, and they like to automate simple, rule-based processes, the so-called âlow hanging fruits,â to take advantage of RPA, which usually takes a few weeks to complete at a manageable level of complexity and cost, In doing so, our customers implicitly take the first step on the stairs towards intelligent automation and are especially accompanied by the introduction of AI methods by the Feat when going through various integration stages from parallel operation to sustainable AI automation. You can find out more about possible integration levels and potentials of artificial intelligence for your company.
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Article Added on Thursday, June 13, 2019
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